This is a topic of direct personal and business relevance, but also of a technical forecasting and measurement perspective. Very little I’ve seen so far gives my confidence in the forecasting, which is either because of poor forecasting or from very limited communication.

This is my first new post in over two years. There are many reasons for that, and I may get into that in a future post. As to why I’m restarting – a conversation with an old friend last night combined with a lunch discussion with an actuarial student a couple of weeks ago has inspired me to attempt to, temporarily at least, restart my blog.

I’m going further than just restarting, I’m committing to a new blog post each day for October. Now the reasons for having stopped blogging haven’t suddenly changed, so it’s likely that some of these posts will be short. (And similarly, some of them long.) Since the decision was made last night, I also haven’t though through anything like a full plan for the month. I invite you along to see how it goes.

I’m probably not alone in being slightly more jaded, slightly less optimistic than I was two years ago. A summary of the two years might make its way into another post, more to help me collect my thoughts than anything else.

Cape Town is experiencing an intense, multi-year drought and there is a real possibility of the city running out of water before next winter. I will definitely be blogging more about the vacuum of credible communication and forecasting on this front in a later post. For now, a single-purpose website http://www.howmanydaysofwaterdoescapetownhaveleft.co.za/ is currently proclaims (they update weekly, I think, based on updated weekly reports of dam levels) that we have 151 days of water left and will run out of useable water on 1 March 2018.

For now, the claims of cholera in Puerto Rico have not been proven, but it does feel like it’s only a matter of time. Anyone fretting over drinking water in Cape Town should probably bump diseases such as cholera up their list.

The official position of the City of Cape Town is still “we won’t run out of water”, but there are reasons to doubt this and be concerned. I’m keen to work out objectively what the level of risk is. To that end, it would have been useful to be able to dissect the http://www.howmanydaysofwaterdoescapetownhaveleft.co.za/ methodology to understand how credible their forecast is. This is the entire disclosure of their methodology:

Using our recent consumption as a model for future usage, we’re predicting that dam levels will reach 10% on the 1st of March, 2018.

The paper isn’t paygated so check it out – it’s only 6 pages so definitely accessible. Don’t worry about the couple of typos in the paper, bizarre as it may be to find them in a paper that presumably was reviewed, the ideas are still good.

The key idea is that prediction markets usually focus on binary events. Will Person Y win the election? Will China invade Taiwan? These outcomes are relatively easy to predict and circumvent important challenges of extreme outcomes and Taleb’s Black Swans.

A quote from the paper, itself quoting Taleb’s book, Fooled By Randomness, sums up the problem of trying to live in. Binary world when the real world has a wide range of outcomes.

In Fooled by Randomness, the narrator is asked “do you predict that the market is going up or down?” “Up”, he said, with confidence. Then the questioner got angry when he discovered that the narrator was short the market, i.e., would benefit from the market going down. The trader had a difficulty conveying the idea that someone could hold the belief that the market had a higher probability of going up, but that, should it go down, it would go down a lot. So the rational response was to be short.

Any model is a simplification of reality. If it isn’t, then it isn’t a model as rather is the reality.

A MODEL ISN’T REALITY

Any simplified model I can imagine will also therefore not match reality exactly. The closer the model gets to the real world in more scenarios, the better it is.

Not all model parameters are created equal

Part of the approach to getting a model to match reality as closely as possible is calibration. Models will typically have a range of parameters. Some will be well-established and can be set confidently without much debate. Others will have a range of reasonable or possible values based on empirical research or theory. Yet others will be relatively arbitrary or unobservable.

We don’t have to guess these values, even for the unobservable parameters. Through the process of calibration, the outputs of our model can be matched as closely as possible to actual historical values by changing the input parameters. The more certain we are of the parameters a priori the less we vary the parameters to calibrate the model. The parameters with most uncertainty are free to move as much as possible to fit the desired outputs.

During this process, the more structure or relationships that can be specified the better. The danger is that with relatively few data points (typically) and relatively many parameters (again typically) there will be multiple parameter sets that fit the data with possibly only very limited difference in “goodness of fit” for the results. The more information we add to the calibration process (additional raw data, more narrowly constrained parameters based on other research, tighter relationships between parameters) the more likely we are to derive a useful, sensible model that not only fits out calibration data well but also will be useful for predictions of the future or different decisions.

It’s chock-full of analysis, numbers, tables and charts showing how as much as things change, the scope for financial crises changes very little. The comparison of Developed and Emerging Markets is particularly interesting in that the differences, while they do exist, are far smaller than stereotypical views. Emerging Markets do tend to have more ongoing sovereign defaults, but the frequency of banking crises is little different. Weirdly, some aspects of Emerging Market crises (such as employment impacts) are less than average for the Developed World.

It isn’t really the book’s fault, but this was one of the few books that I struggled with on my kindle – the graphs and charts and captions to figures were particularly difficult to read. Perhaps they would look better on the Kindle DX (the larger model) or even an iPad or something.

Although the book doesn’t focus on the current (still-happening, if you weren’t paying attention) financial crisis, there are several chapters dedicated to it with an analysis of the economic indicators leading up to the crash. Now it’s incredibly easy to predict an event after it’s happened, but I’m still hopeful that the results can be useful in predicting future problems and potentially impacting economic policies and regulations for the better.

Some key conclusions from the book for predictors of financial crises:

markedly raising asset prices (yes, and in particular house prices given the likely co-factor of increases in debt levels)

This is not the best way to start serious analysis of models versus markets in the prediction space, but given that I’m writing an exam tomorrow I thought I should put the links out there now. I’ll address this topic again in the future.

I have a clear strategy for how not to lose money playing the Make a Million competition. As I explain it, you may come up with some smart tactics to win the competition and enhance your returns, but you’re on you’re own there.

So, how does one not lose money with the Make a Million competition?

Don’t enter.

You are overwhelmingly like to lose money if you enter this competition. I’ve said this before, and I’ve been right before. I’m right again.

Looks like my money is safe – Reserve Bank cut rates as predicted. Thinking about trying to predict for each MPC meeting then tracking my performance over time so I can be held accountable. Will mull over this first I am not that sure I’ll be sufficiently confident to stick my neck out in future!